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While the state-of-the-art image composition algorithms subtly handle the object boundary to achieve seamless image copy-and-paste, it is observed that they are unable to preserve the color fidelity of the source object, often require quite an amount of user interactions, and often fail to achieve realism when there exists salient discrepancy between the background textures in the source and destination images. These observations motivate our research towards color controlled natural and seamless image composition with least user interactions. In particular, based on the Poisson image editing framework, we first propose a variational model that considers both the gradient constraint and the color fidelity. The proposed model allows users to control the coloring effect caused by gradient domain fusion. Second, to have less user interactions, we propose a distance-enhanced random walks algorithm, through which we avoid the necessity of accurate image segmentation while still able to highlight the foreground object. Third, we propose a multiresolution framework to perform image compositions at different subbands so as to separate the texture and color components to simultaneously achieve smooth texture transition and desired color control. The experimental results demonstrate that our proposed framework achieves better and more realistic results for images with salient background color or texture differences, while providing comparable results as the state-of-the-art algorithms for images without the need of preserving the object color fidelity and without significant background texture discrepancy.